The monitoring and detection of contaminants and other abnormal conditions in water systems is now a necessity in the United States and around the world. Ensuring a water system to be clean and safe has become a more visible public issue since the terrorist attacks of Sep. 11, 2001. Concern that water safety may be affected by potential terrorist activities has increased the awareness and emphasis on developing and deploying new sensing, analytical and decision making technologies, and in particular, ones that make possible near-real time (NRT) monitoring and management of water quality.
Traditionally, water has been tested for presence of contaminants by taking a sample, such as filling a container with the water to be tested, and then transporting that sample to a remote laboratory for analysis. The results are then reported back to the operating entity. By the time results are available, the actions available to the agency responsible operating the water system will likely be limited to remedial actions, including costly measures to clean up the affected water system. If the contaminant is toxic, the lag time in response could contribute to catastrophic health results.
It is therefore essential to quickly and accurately detect and identify in near real-time a wide range of contaminants, including chemicals and radiological and infectious biological agents. Such a system must detect contaminants at very low concentration levels in water. The increased complexity of the sensor arrays necessary for near real time detection at low concentrations raises a new issue: the need to integrate and interpret multiple data sources rapidly and determine the correct response for the affected water system. The total amount of information available from multiple sensor arrays may be too complex for the end user to interpret in the time allowed. This generally causes the water system operator to ignore some information to focus on data that is most familiar, and may lead to erroneous interpretations of the available data.
There are some examples in the prior art of water quality fluorometer sensors. The method described in U.S. Pat. No. 6,064,480 entitled, “Method Of Optical Particle Counting For Water Mixed Lubricant,” issued on May 16, 2000 to Mountain et al., is confined to monitoring solid particles greater than about 5 microns in size. A light detector generates an electrical signal responsive to the passage of a light obstructing particle between the light detector and a light emitter. The apparatus described in U.S. Pat. No. 6,141,097 entitled, “Optical Measurement Of Marine Conditions,” issued on Oct. 31, 2000 to Herman, is confined to detecting organisms or particle sizes above about 2.5 μm. The apparatus uses an optical system in which the receiver comprises an array of photo-sensor elements wherein the size of the photo-sensor elements is selected to be greater than the smallest organism and smaller than the largest organism to be measured. This apparatus generates an output signal providing an average level representative of turbidity in the water and a changing attenuated level caused by the passage of an organism.
The method described in U.S. Pat. No. 6,255,118 B1 entitled, “Method For Using An All Solid-State Fluorometer In Industrial Water System Applications,” issued on Jul. 3, 2001 to Fehr et al., is confined to the monitoring of fluorescent tracers that are particularly suitable for industrial water sample stream applications. A solid-state diode laser is used to excite the fluorescent tracers and a photodiode detects the scattered light. The output from the photodiode is amplified to produce an output voltage proportional to the quantity of fluorescence striking the photodiode detector.
Notwithstanding the usefulness of the prior art, what is needed is a system that analyzes a plurality of various sensor signals to detect in real-time or near real-time the presence of any of a number of organic and chemical compounds that pose a threat to water systems, the system having built-in redundancies and near real-time communication capabilities. What also is needed is a system that can integrate a large amount of analytical data provided by sensors, evaluate the data, predict water quality of a given water system, and provide an alert that is triggered in the event a hazardous condition is detected.